A Neural Network Subgrid Model of the Early Stages of Planet Formation

Planet formation is a multi-scale process in which the coagulation of μm-sizeddust grains in protoplanetary disks is strongly influenced by the hydrodynamicprocesses on scales of astronomical units (≈ 1.5 × 10^9 km). Studies are thereforedependent on subgrid models to emulate the micro physics of dust coagulationon top of a large scale hydrodynamic simulation. Numerical simulations whichinclude the relevant physical effects are complex and computationally expensive.Here, we present a fast and accurate learned effective model for dust coagulation,trained on data from high resolution numerical coagulation simulations. Our modelcaptures details of the dust coagulation process that were so far not tractable withother dust coagulation prescriptions with similar computational efficiency